Fundus image enhancement using visual transformation and maximum a posterior estimation

Abstract

Fundus imaging is used to diagnose many common diseases in human vision. Because of uneven brightness, blur effect, and low contrast, fundus images are not helpful for diagnosis. The proposed technique provides an effective enhancement technique for fundus images. The method performs the appropriate illumination improvement, details correction, and denoising operations. A maximum a posterior (MAP) estimator is used to calculate the illumination and reflection components, which is motivated by the retinex method. The estimated illumination part is put through gamma correction to smooth its unevenness. A straightforward visual transformation algorithm is applied to address the unequal luminosity at the structural layer; it is used to enhance the details of the structure layer. The suggested methods were examined on public datasets (STARE, CHASEDB1, and DRIVE). To further confirm the recommended strategy's benefit in aiding the diagnosis, ophthalmologists' quality assessments were also taken. The objective evaluation used performance parameters such as GMSD, NIQE, HPSI, VSI, MCSD, and PSNR. According to experimental results, the suggested method may simultaneously manage the duties of illumination enhancement, details improvement, noise, and artifact suppression, outperforming state-of-the-art methods. According to the qualitative, quantitative, and statistical examination, the suggested algorithm reliably improves low-contrast retinal photos with intact color and a natural appearance.